Beyond Tasks: A Vision for Reproducing an Animal-like Behavioral Substrate Using Modern Robot Learning Techniques
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Abstract (arXiv)
Recent advances in robot learning have produced increasingly capable embodied agents. Yet comparatively less attention has been given to a more basic form of competence that animals exhibit continuously: the ability to remain situated, responsive, and behaviorally coherent as physical, environmental, and social demands change over time. We propose the ethological behavioral substrate as a conceptual lens for studying this form of competence in artificial agents. Rather than treating these behaviors that animals exhibit as a set of isolated skills, we argue that their continual coordination under competing demands constitutes an important and underexplored target for modern robot learning. We further propose robotic animal companions as a useful research setting for studying sustained interaction and adaptation in human-centered environments. Such systems provide an opportunity to investigate how social behavior, memory, and continual learning develop over long periods of interaction. This perspective motivates further investigation of how such persistent behavioral competence may complement higher-level capabilities in embodied agents.
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References · 10
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,080
- 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,887
- 2024Octo: An Open-Source Generalist Robot Policycited by 1,484
- 2022DayDreamer: World Models for Physical Robot Learningcited by 400
- 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 108
- 2023QUAR-VLA: Vision-Language-Action Model for Quadruped Robotscited by 56
- 2018Who Let The Dogs Out? Modeling Dog Behavior From Visual Datacited by 20
- 2025Olaf: Bringing an Animated Character to Life in the Physical Worldcited by 8
- 2022FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2cited by 6
- 2025Emotions in the Loop: A Survey of Affective Computing for Emotional Supportcited by 5
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